Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2005


 Y. Alexeev, B.A. Allan, R.C. Armstrong, D.E. Bernholdt, T.L. Dahlgren, D. Gannon, C.L. Janssen, J.P. Kenny, M. Krishnan, J.A. Kohl, G. Kumfert, L.C. McInnes, J. Nieplocha, S.G. Parker, C. Rasmussen, T.L. Windus. “Component-Based Software for High-Performance Scientific Computing,” In Journal of Physics: Conference Series, Vol. 16, IOP Publishing, pp. 536--540. January, 2005.
DOI: 10.1088/1742-6596/16/1/073


O. Alter, G.H. Golub. “Reconstructing the Pathways of a Cellular System from Genome-Scale Signals by Using Matrix and Tensor Computations,” In Proceedings of the National Academy of Sciences, Vol. 102, No. 49, Proceedings of the National Academy of Sciences, pp. 17559--17564. November, 2005.
DOI: 10.1073/pnas.0509033102


A.E. Anderson, C.L. Peters, B.D. Tuttle, J.A. Weiss. “A Subject-Specific Finite Element Model of the Pelvis: Development, Validation and Sensitivity Studies,” In ASME J. Biom. Eng., Vol. 127, No. 3, pp. 364--373. 2005.


S.P. Awate, T. Tasdizen, R.T. Whitaker. “Nonparametric Statistics of Image Neighborhoods for Unsupervised Texture Segmentation,” SCI Institute Technical Report, No. UUSCI-2005-003, University of Utah, 2005.


S.P. Awate, R.T. Whitaker. “Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering,” SCI Institute Technical Report, No. UUSCI-2005-004, University of Utah, 2005.


S.P. Awate, R.T. Whitaker. “Nonparametric Neighborhood Statistics for MRI Denoising,” SCI Institute Technical Report, No. UUSCI-2005-005, University of Utah, 2005.


S.P. Awate, R.T. Whitaker. “Nonparametric Neighborhood Statistics for MRI Denoising,” In In Proc. Int. Conf. Information Processing in Medical Imaging (IPMI), 2005. Lecture Notes in Computer Science, Vol. 3565, pp. 677--688. July, 2005.


S.P. Awate, R.T. Whitaker. “Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering,” In Proceedings of the IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), pp. 44--51. 2005.


S.P. Awate, R.T. Whitaker. “Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration,” In IEEE Trans. Pattern Anal. & Mach. Intel., Vol. 28, No. 3, pp. 364--376. 2005.


L. Barbosa, A.C. Salgado, F. de Carvalho, J. Robin, J. Freire. “Looking at both the Present and the Past to Efficiently Update Replicas of Web Content,” In Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, pp. 75--80. 2005.


D. Barbosa, J. Freire, A. Mendelzon. “Designing Information-Preserving Mapping Schemes for XML,” In Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, pp. 109--120. 2005.


L. Bavoil, S.P. Callahan, P.J. Crossno, J. Freire, C.E. Scheidegger, C.T. Silva, H.T. Vo. “Vistrails: Enabling Interactive Multiple-View Visualizations,” In Proceeding of IEEE Visualization 2005, pp. 18. 2005.


F. Bertrand, R. Bramley, K. Damevski, D. Bernholdt, J. Kohl, J. Larson, A. Sussman. “Data Redistribution and Remote Method Invocation in Parallel Component Architectures,” In Proceedings of The 19th International Parallel and Distributed Processing Symposium, Vol. 1, Note: Awarded Best Paper, pp. 40--42. 2005.


F.F. Bernardon, S.P. Callahan, J.L.D. Comba, C.T. Silva. “Volume Rendering of Time-Varying Scalar Fields on Unstructured Meshes,” SCI Institute Technical Report, No. UUSCI-2005-006, University of Utah, 2005.


M. Berzins, R.M. Kirby, C.R. Johnson. “Integrating Teaching and Research in HPC: Experiences and Opportunities,” In Proceedings of the International Conference on Computational Science (ICCS) 2005, Atlanta, GA, pp. 36--43. 2005.


M. Berzins. “Preserving Positivity for Hyperbolic PDEs Using Variable-Order Finite Elements with Bounded Polynomials,” In Applied Numerical Mathematics, Vol. 52, No. 2-3, pp. 197--217. February, 2005.


C. Bonifasi-Lista, S.P. Lake, M. Small, J.A. Weiss. “Viscoelastic Properties of the Human Medial Collateral Ligament Under Longitudinal, Transverse and Shear Loading,” In J. Orthoped. Res., Vol. 23, No. 1, pp. 67--76. January, 2005.


P.-T. Bremer, V. Pascucci, B. Hamann. “Maximizing Adaptivity in Hierarchical Topological Models,” In International Conference on Shape Modeling and Applications 2005, IEEE, 2005.
DOI: 10.1109/smi.2005.28


E. Bullitt, D. Zeng, G. Gerig, S. Aylward, S. Joshi, J.K. Smith, W. Lin, M. Ewend. “Vessel Tortuosity and Brain Tumor Malignancy: A Blinded Study,” In Acad Radiol, Vol. 12, No. 10, pp. 1232--1240. October, 2005.


C.R. Butson, C.C. McIntyre. “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation,” In Clinical Neurophysiology, Vol. 116, No. 10, pp. 2490--500. October, 2005.
DOI: 10.1016/j.clinph.2005.06.023
PubMed ID: 16125463

ABSTRACT

OBJECTIVE: The growing clinical acceptance of neurostimulation technology has highlighted the need to accurately predict neural activation as a function of stimulation parameters and electrode design. In this study we evaluate the effects of the tissue and electrode capacitance on the volume of tissue activated (VTA) during deep brain stimulation (DBS).

METHODS: We use a Fourier finite element method (Fourier FEM) to calculate the potential distribution in the tissue medium as a function of time and space simultaneously for a range of stimulus waveforms. The extracellular voltages are then applied to detailed multi-compartment cable models of myelinated axons to determine neural activation. Neural activation volumes are calculated as a function of the stimulation parameters and magnitude of the capacitive components of the electrode-tissue interface.

RESULTS: Inclusion of either electrode or tissue capacitance reduces the VTA compared to electrostatic simulations in a manner dependent on the capacitance magnitude and the stimulation parameters (amplitude and pulse width). Electrostatic simulations with typical DBS parameter settings (-3 V or -3 mA, 90 micros, 130 Hz) overestimate the VTA by approximately 20\% for voltage- or current-controlled stimulation. In addition, strength-duration time constants decrease and more closely match clinical measurements when explicitly accounting for the effects of voltage-controlled stimulation.

CONCLUSIONS: Attempts to quantify the VTA from clinical neurostimulation devices should account for the effects of electrode and tissue capacitance.

SIGNIFICANCE: DBS has rapidly emerged as an effective treatment for movement disorders; however, little is known about the VTA during therapeutic stimulation. In addition, the influence of tissue and electrode capacitance has been largely ignored in previous models of neural stimulation. The results and methodology of this study provide the foundation for the quantitative analysis of the VTA during clinical neurostimulation.

Keywords: Algorithms, Axons, Axons: physiology, Computer Simulation, Deep Brain Stimulation, Electric Capacitance, Electrodes, Extracellular Space, Extracellular Space: physiology, Finite Element Analysis, Implanted, Models, Myelinated, Myelinated: physiology, Nerve Fibers, Neurological, Neurons, Neurons: physiology, Poisson Distribution, Statistical